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     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Scientific Programming in Python (SPiP) - Mathplot Exercise 5.1\n",
      "\n",
      "by [Michael Granitzer (michael.granitzer@uni-passau.de)](http://www.mendeley.com/profiles/michael-granitzer/) \n",
      "\n",
      "   \n",
      "__License__\n",
      "\n",
      "This work is licensed under a [Creative Commons Attribution 3.0 Unported License](http://creativecommons.org/licenses/by/3.0/)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Overview\n",
      "\n",
      "The aim of this exercise is to practice the use of mathplotlib for plotting multivariate data in various subplots. We will use the well known Irisi Data Set, which is introduced next."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# The Iris Data Set\n",
      "\n",
      "The [Iris data set](http://archive.ics.uci.edu/ml/datasets/Iris) (often also called Fishers Iris Data) is a well known data set in data mining. The data set consists of 50 samples from each of three species of the Iris flowwer (Iris setosa, Iris virginica and Iris versicolor). For every flower four measures have been taken: the length and the width of the sepals and petals measured in centimetres (see also http://en.wikipedia.org/wiki/Iris_flower_data_set)\n",
      "\n",
      "An example of the three different Iris flowers (source Wikipedia):\n",
      "\n",
      "* Setosa \n",
      "<img src=\"http://en.wikipedia.org/wiki/File:Kosaciec_szczecinkowaty_Iris_setosa.jpg\">\n",
      "\n",
      "* Versicolor  \n",
      "<img src=\"http://en.wikipedia.org/wiki/File:Iris_versicolor_3.jpg\">\n",
      "\n",
      "* Virginica \n",
      "<img src=\"http://en.wikipedia.org/wiki/File:Iris_virginica.jpg\">"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Sequenz for downloading and importing the iris data set\n",
      "import numpy as np\n",
      "import urllib2 #need for web access\n",
      "req = urllib2.urlopen('http://mlpy.sourceforge.net/docs/3.2/_downloads/iris1.csv')\n",
      "iris = np.loadtxt(req, delimiter=',')\n",
      "# data: (observations x attributes) matrix, classes: classes (1: setosa, 2: versicolor, 3: virginica)\n",
      "data, classes = iris[:, :4], iris[:, 4].astype(np.int) \n",
      "print \"Dataset Description: (Please note that in our version the last attribute is numeric to work with numpy)\"\n",
      "print \"=======================================================================================================\\n\"\n",
      "print urllib2.urlopen('http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.names').read()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Dataset Description: (Please note that in our version the last attribute is numeric to work with numpy)\n",
        "=======================================================================================================\n",
        "\n",
        "1. Title: Iris Plants Database\n",
        "\tUpdated Sept 21 by C.Blake - Added discrepency information\n",
        "\n",
        "2. Sources:\n",
        "     (a) Creator: R.A. Fisher\n",
        "     (b) Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
        "     (c) Date: July, 1988\n",
        "\n",
        "3. Past Usage:\n",
        "   - Publications: too many to mention!!!  Here are a few.\n",
        "   1. Fisher,R.A. \"The use of multiple measurements in taxonomic problems\"\n",
        "      Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions\n",
        "      to Mathematical Statistics\" (John Wiley, NY, 1950).\n",
        "   2. Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.\n",
        "      (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n",
        "   3. Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n",
        "      Structure and Classification Rule for Recognition in Partially Exposed\n",
        "      Environments\".  IEEE Transactions on Pattern Analysis and Machine\n",
        "      Intelligence, Vol. PAMI-2, No. 1, 67-71.\n",
        "      -- Results:\n",
        "         -- very low misclassification rates (0% for the setosa class)\n",
        "   4. Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE \n",
        "      Transactions on Information Theory, May 1972, 431-433.\n",
        "      -- Results:\n",
        "         -- very low misclassification rates again\n",
        "   5. See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al's AUTOCLASS II\n",
        "      conceptual clustering system finds 3 classes in the data.\n",
        "\n",
        "4. Relevant Information:\n",
        "   --- This is perhaps the best known database to be found in the pattern\n",
        "       recognition literature.  Fisher's paper is a classic in the field\n",
        "       and is referenced frequently to this day.  (See Duda & Hart, for\n",
        "       example.)  The data set contains 3 classes of 50 instances each,\n",
        "       where each class refers to a type of iris plant.  One class is\n",
        "       linearly separable from the other 2; the latter are NOT linearly\n",
        "       separable from each other.\n",
        "   --- Predicted attribute: class of iris plant.\n",
        "   --- This is an exceedingly simple domain.\n",
        "   --- This data differs from the data presented in Fishers article\n",
        "\t(identified by Steve Chadwick,  spchadwick@espeedaz.net )\n",
        "\tThe 35th sample should be: 4.9,3.1,1.5,0.2,\"Iris-setosa\"\n",
        "\twhere the error is in the fourth feature.\n",
        "\tThe 38th sample: 4.9,3.6,1.4,0.1,\"Iris-setosa\"\n",
        "\twhere the errors are in the second and third features.  \n",
        "\n",
        "5. Number of Instances: 150 (50 in each of three classes)\n",
        "\n",
        "6. Number of Attributes: 4 numeric, predictive attributes and the class\n",
        "\n",
        "7. Attribute Information:\n",
        "   1. sepal length in cm\n",
        "   2. sepal width in cm\n",
        "   3. petal length in cm\n",
        "   4. petal width in cm\n",
        "   5. class: \n",
        "      -- Iris Setosa\n",
        "      -- Iris Versicolour\n",
        "      -- Iris Virginica\n",
        "\n",
        "8. Missing Attribute Values: None\n",
        "\n",
        "Summary Statistics:\n",
        "\t         Min  Max   Mean    SD   Class Correlation\n",
        "   sepal length: 4.3  7.9   5.84  0.83    0.7826   \n",
        "    sepal width: 2.0  4.4   3.05  0.43   -0.4194\n",
        "   petal length: 1.0  6.9   3.76  1.76    0.9490  (high!)\n",
        "    petal width: 0.1  2.5   1.20  0.76    0.9565  (high!)\n",
        "\n",
        "9. Class Distribution: 33.3% for each of 3 classes.\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Exercise 5.1-a: Visualisation of the Iris Data Set\n",
      "\n",
      "* First load the data set with the above code.\n",
      "* Create a single histogram-plot for every dimension using mathplotlib\n",
      "* Create a 4x4 plot containing the four histograms side by side\n",
      "* Create a single histogram-plot containing all dimensions\n",
      "* Create a boxplot over the four dimensions\n",
      "* Implement a function that creates a scatter plot matrix, where the main diagonal are either boxplots or histograms and classes.\n",
      "* Encode the class information with different colors in the scatter plot matrix\n",
      "\n",
      "In all visualisations above take care off apropriate labelling of axis and a apropriate titles of figures. If you created the most basic form of the above visualisations, try to change the appearance for easier identifying patterns.\n",
      "\n",
      "Also, try to answer the following questions:\n",
      "\n",
      "* What does the distribution of an attribute look like? Are the attribute values reasonable?\n",
      "* Which of the above presentations of histograms allows you to easier compare the distributions of the different attributes?\n",
      "* Which attributes seem to be most discriminative to differentiate the different flowers?\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Solutions\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Solution 5.1-  Visualisation of the Iris Data Set"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**1. Create a single histogram-plot for every dimension using mathplotlib**"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline\n",
      "from pylab import *\n",
      "import matplotlib as plt\n",
      "\n",
      "#to make life easier lets define a helper function\n",
      "def plotHistogramm(x,htitle,num_bins=50):\n",
      "    figure()#create a new figure\n",
      "    # the histogram of the data\n",
      "    n, bins, patches = hist(x, num_bins, normed=0, facecolor='green', alpha=0.5)\n",
      "    title(htitle)\n",
      "    show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "WARNING: pylab import has clobbered these variables: ['plt', 'power', 'draw_if_interactive', 'random', 'fft', 'linalg', 'info']\n",
        "`%matplotlib` prevents importing * from pylab and numpy\n"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plotHistogramm(data[:,0],\"Histogram of Sepal Length\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
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LUgQMc0mKgGEuSREwzCUpAoa5JEXAMJekCBjmkhQBw1ySImCYS1IEDHNJioBhLkkR6PaC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\nSZIkSZIkSZIkSVIR/h8kqglGZzIfCAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10df80410>"
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plotHistogramm(data[:,1],\"Histogram of Sepal Width\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10dfac9d0>"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plotHistogramm(data[:,2],\"Histogram of Petal Length\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10e17dad0>"
       ]
      }
     ],
     "prompt_number": 52
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plotHistogramm(data[:,3],\"Histogram of Petal Width\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10dfcc0d0>"
       ]
      }
     ],
     "prompt_number": 68
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**2.  Create a 4x4 plot containing the four histograms side by side**"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "fig, axes = plt.subplots(nrows=2,ncols=2,figsize=(12,4))\n",
      "fig.tight_layout()\n",
      "\n",
      "names=[\"sepal length\",\"sepal width\", \"petal length\", \"petal width\"]\n",
      "colors=[\"red\",\"green\",\"blue\",\"grey\"]\n",
      "\n",
      "num_bins=50\n",
      "for row in range(len(axes)):\n",
      "    for col in range(len(axes[row])):\n",
      "        idx=row*(len(axes[row]))+col\n",
      "        n, bins, patches = axes[row,col].hist(data[:,idx], num_bins, normed=1, facecolor=colors[idx], alpha=0.5)\n",
      "        axes[row,col].set_title(\"Histogramm of %s\"%(names[idx]))\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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gvwbzu2EfNPsOJTp/P1wBvDOZ3gPYt2Z+p++HZvGX6Px9UDUHeJg4SUzr9H2Q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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1114faf90>"
       ]
      }
     ],
     "prompt_number": 69
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**3.  Create a single histogram-plot containing all dimensions**"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "figure(figsize=(12,3))#create a new figure    \n",
      "n, bins, patches = hist(data, num_bins, normed=1,histtype=\"step\", color=colors, alpha=0.5, label=names)\n",
      "title(\"Normalized histogram of Iris Data Set\")\n",
      "legend()\n",
      "show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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v2U5su0V34/YBtwcoKqOsooypS6by6LmPBrQeEWlZmto4tQ2VkZFBz549KS8v\nr9RnuCWp6TX1JNK15rlWWoIXA0m17L8SeMuzvgyIAToD+2ssISJBV1JeQpe2XRg7cGywQxEREWl0\n/vjaEA/s8tneDXTzw3lFREREpBZN7WazE4m/Roeo+gpU+1tDWlqadz01NbXGITZEREREpHZJSUlU\nVFQEO4wmIT09nfT0dFtl/JEE7wESfLa7eR77Fd8kWERERETEH6o2rk6ZMqXOMv7oDjEXuNmzfhaQ\nh/oDi4iIiEgTZqUleA6QAnTE9P2dDIR59k0HvsCMDLEVKARu9X+YIiIiIiL+YyUJHm3hmOqnDhER\nERERaYJa5qByIiIiItKiKQkWERERaYGcTifbt2+vdl9qaiqzZs1q5IiM2uLyJ38NkSYiIiJy4pk6\nFYqKAnf+iAh46KHAnT9AHA5Ho4xBnJqayk033cTttwd2JtLqKAkWERGRlquoCAI5hKuGh61VMCf7\nUHcIERERkSCbOnUq3bp1Izo6mj59+vDtt98C4Ha7eeaZZ0hOTqZjx46MHDmS3NxcADIyMnA6ncyc\nOZP4+Hji4uJ4/vnnvedcvnw5Q4cOJTY2lri4OCZMmEBZWVm94ps9ezZ9+/alffv2jBgxgszMTO8+\np9PJ9OnTOeWUU4iNjWX8+OPjJbhcLv785z9z0kkn0bNnT1555RWcTicVFRVMmjSJxYsXM378eKKi\norj33nu95b755ptqz+dPSoJFREREguiXX37h1VdfZeXKlRw5coT58+eTlJQEwMsvv8zcuXNZtGgR\ne/fuJTY2lnvuuadS+fT0dLZu3cr8+fOZOnUqCxYsACA0NJSXXnqJQ4cOsXTpUhYsWMBrr71mO75P\nP/2Up59+mk8++YSDBw8yfPhwRo+uPHjY559/zsqVK1m7di0ffvghX3/9NQAzZszgq6++Ys2aNfz0\n00/8+9//9na1ePLJJxk+fDivvvoq+fn5vPzyy3Wez5+UBIuIiIgEUUhICCUlJaxfv56ysjISExPp\n2bMnANOnT+eJJ54gLi6OsLAwJk+ezMcff4zL5fKWnzx5MhEREZx66qnceuutzJkzB4ABAwYwZMgQ\nnE4n3bt3Z+zYsSxcuNB2fK+//joTJ06kd+/eOJ1OJk6cyOrVq9m1a5f3mIcffpjo6GgSEhL43e9+\nx5o1awD48MMP+dOf/kRcXBwxMTFMnDgRt9td6fxVt6s73+rVq23HXRclwSIiIiJBlJyczIsvvkha\nWhqdO3eVxL4/AAAbAElEQVRm9OjR7N27FzBdHq6++mpiY2OJjY2lb9++hIaGsn//8cl5ExISvOuJ\niYlkZWUBsHnzZi6//HK6du1Ku3btmDRpEocOHbId386dO7nvvvu8MXTo0AGAPXv2eI/p0qWLdz0y\nMpKCggIA9u7dWym+bt26/er81fULrul8/qQkWERERCTIRo8ezeLFi9m5cycOh4OHPCNKJCYm8tVX\nX5Gbm+tdjh49SteuXb1lffvnZmZmEh8fD8Bdd91F37592bp1K4cPH+bJJ5+s1IJsVWJiIjNmzKgU\nQ2FhIWeddVadZbt27Vqpxdh3HXRjnJwAXC4XJSUlFBcXW15KS0ur/YlDREREjtu8eTPffvstJSUl\nhIeH07p1a0JCQgC48847eeSRR7yJbnZ2NnPnzq1U/oknnqCoqIj169fz5ptvMnLkSAAKCgqIiooi\nMjKSTZs2MW3atHrFd+edd/LUU0+xYcMGAA4fPsxHH31U4/Fut9v7+X/dddfx0ksvkZWVRV5eHlOn\nTq2U+Hbu3Jlt27bVWn+gcgkNkSaW7Nmzh9atD5Cfb/2u0q1bo+nevSsQG7jAREREGiIiIrDDmEVE\n1HlISUkJEydOZOPGjYSFhXHOOecwY8YMAO677z7cbjcXXXQRWVlZdOrUiVGjRnHllVd6y6ekpJCc\nnIzL5eIvf/kLF1xwAQDPPfccY8eO5dlnn+WMM85g1KhRfPfdd95yVlthr7rqKgoKChg1ahQ7d+6k\nXbt2XHTRRVx77bXVnsd3jOE77riDzZs3c/rpp9OuXTsmTJjAwoULcTqd3ud3yy23MG3aNG6++WZe\nfPHFX9UfqDGLG7MN2l1TJl9QUMDGjRsZPHiw5ZMVFxezatUqhg4d6q/4pBZPPpnJxRdHM2hQjOUy\nL7ywi0GDIjn33A4BjEzqa8+RPXy+5XPGDhwb7FB+payijKlLpvLouY8GOxQRaUYcDkez+oUyIyOD\nnj17Ul5e7k0qm7ovv/ySu+66i4yMDL+cr6bX1JM015rnWrliI4BNwBaguilPOgJfAauBn4ExFs4p\nIiIiIs1ccXExX3zxBeXl5ezZs4cpU6ZwzTXXBDssoO4kOAR4BZMI9wVGA7+pcsx4YBXQH0gFnkfd\nLEREREQCLpg3llnhdrtJS0ujffv2DBgwgH79+vH4448HOyyg7mR1CLAVyPBsvw/8Htjoc8xe4HTP\nejRwCCj3X4giIiIiUlVSUhIVFRXBDqNWERERLF++PNhhVKuuJDge8B3LYjdwZpVjZgLfAllAFHCd\n36ITEREREQmAupJgK73HH8H0B04FegHfAL8F8qsemOZz92VqaiqpqanWohQRERERqUF6ejrp6em2\nytSVBO8BEny2EzCtwb7OBp70rG8DdgC9gZVVT5YWyCFIRERERKRFqtq4OmXKlDrL1JUErwROBpIw\n3R1GYm6O87UJuABYAnTGJMDbrYUswVBaWkppaamtMvWZYUakseUV51FSXmKrTKgzlA6RGsZPpCWI\njY1t8jeSiT2xsfWfi6CuJLgcM/rD15iRImZhboob59k/HXgKeANYgxlt4kEgp94RScCtX7+ekpIS\n72w0VrjdHWjVqlUAoxJpuFeXv0psRCwOG0OgZx/N5sFzHqR1aOsARiYiTUFOjtITOc7KUGZfehZf\n033WDwJX+C0iCTi3203fvn2Jjo62XGbTJoiMDGBQIn5Q4a5g3MBxhDitf8F7dsmzuNz6pUNEpKU5\nMaYXERERERHxIyXBIiIiItLiKAkWERERkRZHSbCIiIiItDhKgkVERESkxVESLCIiIiItjpJgERER\nEWlxlASLiIiISIujJFhEREREWhwlwSIiIiLS4igJFhEREZEWR0mwiIiIiLQ4VpLgEcAmYAvwUA3H\npAKrgJ+BdH8EJiIiIiISKKF17A8BXgEuAPYAK4C5wEafY2KAV4GLgd1AR/+HKSIiIiLiP3W1BA8B\ntgIZQBnwPvD7KsdcD/wTkwADHPRjfCIiIiIifldXS3A8sMtnezdwZpVjTgbCgO+AKOAl4G1/BVgb\nl8vFoUOHbJUJCwsjOjo6QBFJQxUXF1NYWGi7XHR0NGFhYQGI6MRQ7ipnR+4O3Lgtlzl01N7fjogE\nicsF27ebf+1o2xbi4gITk0gzUFcSbOUTNQwYAJwPRAJLgR8wfYgrSUtL866npqaSmppqMcxqKg0L\no127dmRlZVku43K5yM/PZ9iwYfWuVwJr586dHDlyhNatW1suU1hYSHx8PAkJCQGMrGnbdXgXn2z6\nhG7R3WyV69OxT4AiEhG/OXgQPvwQkpKslykrg9xc+NOfAhaWSFOSnp5Oenq6rTJ1JcF7AN/MIoHj\n3R6O2YXpAlHkWRYBv6WOJLihQkJCOPXUU22VKSsrY9myZX6LQQKjW7dudO3a1fLxW7duDWA0JwY3\nbrq07cL1p10f7FBExN/cboiNhett/H3n5cEbbwQuJpEmpmrj6pQpU+osU1ef4JWY7g5JQCtgJObG\nOF+fAsMwN9FFYrpLbLAWsoiIiIhI46urJbgcGA98jUlyZ2FGhhjn2T8dM3zaV8BawAXMREmwiIiI\niDRhdSXBAF96Fl/Tq2w/51lEREROPLt2wbx5puuBHX36wHnnBSam5uzdd+HwYXtlQkPhllsgPDww\nMUmLYyUJFhERad7y8iAqCi680HqZnTthy69ufxErdu6Em28GO6P6/OMfUFKiJFj8RkmwiIgIQOvW\n0Lmz9ePttmRKZSedZC+hDQkJXCzSIlmZNllEREREpFlRS7C0SCUlJeTl5dkuFx0dTURERAAiEhER\nkcakJFhapL1795KdnU2bNm0slzl69CgxMTEkJycHMDIRERFpDEqCpcXq2LEjPXr0sHz87t27KS4u\nDmBEIiIi0ljUJ1hEREREWhwlwSIiIiLS4qg7hEgA5ebmsmnTJtvlOnfuTM+ePQMQkYh//PADLF1q\nv9wFF8Bpp9ko8PPP8M039is680w4+2z75UQ+/RS2b7dXxuGAUaOgS5fAxCQBoSRYJICKi4uJjo6m\nV69elsvk5OSQk5MTwKhEGi4nBwYMgP79rZdZsgRyc21WlJcHJ58Mw4dbL7N2rQlQpD6ys2HECIiL\ns17mX/+CgoLAxSQBoSRYJMBCQkJo3bq15ePD7MygJBJEERHQrp314+s90Vd4uL2KIiI0kYU0TNu2\n9t5z+n/7hKQ+wSIiIiLS4lhpCR4BvAiEAP8LTK3huMHAUuA64F9+iU5ERKQ+DhyAFSssH16yI4vd\nOyDHehHCd0L8IehQj/BEJPjqSoJDgFeAC4A9wApgLrCxmuOmAl8BDj/HKCIiYl1iIuzbB/v3Wy6y\n90AI/3ekN7HWi+DKgPztcI79CEWkCagrCR4CbAUyPNvvA7/n10nwBOBjTGuwiIhI8HTqBJdfbqtI\n/jqI+MVesR1hkGVzEAERaTrq6hMcD+zy2d7teazqMb8Hpnm23f4JTUREREQkMOpqCbaS0L4IPOw5\n1kEt3SHS0tK866mpqaSmplo4vYiIiIhIzdLT00lPT7dVpq4keA+Q4LOdgGkN9jUQ000CoCNwCVCG\n6TtciW8SLMHzzTcxfPppK2yM2kVZGaSkBC4mkRPJW6vfIis/y1YZp8PJ2IFjiY2IDVBUzdPWrbBi\nEWSstFfu7LObz/9ZR47AD/PhxzzrZcKL4JwtcKadisrK4OWXobTUXoCRkXDfffbKiPhZ1cbVKVOm\n1FmmriR4JXAykARkASOB0VWO8Z3W6g3gM6pJgKXpKCgI4b/+q4zeva1nwQ5HA8b4FGlm8orzuPWM\nW4lpHWO5zBur3qC4vDiAUTVPR49CcjJc/f+sl1m1Cg4dClxMja2kBFqFw/+zcQ2OZMKGB21WVFFh\nEmA7Fbnd8OyzNisSaRrqSoLLgfHA15gRIGZhboob59k/PXChSSC1aoWtlmARqaxVSCtah9r5IqmB\nc+rL6bT3/1VznLfA6bB3DUrq22jhsFmRW7cByYnLyjjBX3oWXzUlv7c2LBwRERERkcDTtMkiTUxh\nWSE/HfyJvEzrHQAPHW1Gv/02U/v2wbLD0NFGC53DAQMH6lcbEZFAUBIs0sTsyd/DxryNxJXHWS7T\nplUbTulwSgCjkobavBkSE6Ctjfvi1q2D+HhISgpYWCIiLZaSYJEmqEN4By7oeUGwwxA/GzAA+vey\nfvzuqmPxiIiI39Q1WYaIiIiISLOjJFhEREREWhx1h2hCVqxYQWFhoa0yDoeDM888k9ZN8M6Z8HAX\nn33Wmm+/tV7m4MEOXHedm65dAxdXY9qSt4UP139Ix30dLZcpLCwkMSwxgFGJiD+4w1sTtX8rBHgi\nqIgCKA9LslXGHdaKViUF9mOLbcKTuUREwAsvBL4ep1MD47cQSoKbkLKyMoYOHUq4jT++5cuXU1FR\nEcCo6i81tYCTToqgU6c2lstMm1ZMUVHz+c+nuLyYPjF9GJ863nKZ7Oxs9u/fH8CoRMQfyuMSWXfN\nZE6/IbD1FO6HX/4F59kpFBnJsose42wb8140eXfdFewIpJlRdwgRERERaXGUBIuIiIhIi6PuECLS\nJLncLv6z/T+2yrg1hSubN0Nmpv1ygwZBTIz/45EWwO2G/9j7W6WJduOTlkVJsIg0OaHOUEYkj6Ck\nvMRWuUtPvhSno2X/wPXjj+aenpNOsl5m/XpzvJJgsc3hgMsvh6Iie+UuughatQpMTCIWKQkWkSbH\n4XAwJH5IsMM4YfXtC336WD8+OztwsUgLMGhQsCMQqRerTSYjgE3AFuChavbfAKwB1gJLgNP9Ep2I\niIiISABYaQkOAV4BLgD2ACuAucBGn2O2A+cChzEJ8wzgLL9GKlILl8tFeXm5reMdDkcAIxJp5lwu\nHKVlYKPHiqMUymlFSYn1vz11HRU5QbjdUFpqv1xYmBmbOQisJMFDgK1Ahmf7feD3VE6Cl/qsLwO6\n+SM4ESvCw8PJyMgg0+bdQL169QpQRCLNX4c139J11w+wOMRymVMyylngvJAXlltvI+mWAYP61SNA\nEWlcq1bB559DqI2ethUVMGAAXHpp4OKqhZVI44FdPtu7gTNrOf524IuGBCViR0JCAgkJCcEOQ6RF\ncZaXcvjMi+h2q/W+290WLOCWsFLzu6FV3wM277kSkSAoLTX9wy+5xHqZVatg587AxVQHK0mwnTGH\nfgfcBpxT3c40n+kbU1NTSU1NtXFqEREREZFfS09PJz093VYZK0nwHsC3mS0B0xpc1enATEyf4Nzq\nTpQW4PnVRURERKTlqdq4OmXKlDrLWEmCVwInA0lAFjASGF3lmETgX8CNmP7DIgDs3buXvLw8y8cX\nFsLGjW2xcY8bYLoUde1qMzgR8Vq9GnZX17xRA/d+6JEYuHgaaudO0z3RqkOHIDLSfj0HDtirByA+\nHvr3t19XoLlcZs6LsjJ75SIi4LzzAhPTCWX5cvjlF3tlzj4bYmMDE4/UyUoSXA6MB77GjBQxC3NT\n3DjP/unAX4FYYJrnsTLMDXXSgiUkJJCfn2+rzPnnOykqakNYmPUyGzbAjh1KgkXqa+hQewkwQLtT\nIC4pIOE0WO/e5kZ1Ozp1MsmpHYmJMHy4vbpycuCnn5pmElxaavK4iy+2Xsbthi+/VBLMuefC/v32\nyvz0E2RlKQkOIqu38H3pWXxN91n/o2cR8YqOjiY6OtpWGbsfQmA+VESk/rp2rceXyGygTSCiabio\nKBg8OPD1tG5tf56IzEzYsycw8fhDaKi9a3csCW7xEhPNYkdGRkBCEeta9vyiIiIiItIiadrkZqC0\ntJSQEOtjdbrt/k4oALjcLnKP5rL/sPWfvApLCutXl8tFcXGxrTJhYWG23gdy3JGSI5RVWO8I6XK7\n6lVPYVkBh4sPWz6+yOWkoKAthw9bn1zCbn96b6FCm+/VEhuzZEgl5eVw2PrbgIKC+tXjctmrp74v\nqdttrx4w8yPUpw92i1efv1UwP48EaUKKpkxJ8AmuTZs2bNq0yVYZl6sToXYGsxYAfjnyC/9c909a\nrW9lq9zwXsNtHR8eHk5hYSGrVq2yXMblchEdHc1pp51mqy6BTm068d6692yVCXWG0jq0ta0ybZ0n\n8c3Oz1hW7dg51fupopC9X91KjNP6/EMOh/m8s+WLL2DjRmx1xgfo29dmRRIZCUePwqxZ9srZ/aU9\nPNy8nHbr6dzZ3vHHytitp6AAJk60/5Zr8ebPh7VroZWNz6HiYrjgAhiiW7WqUiZ0guvXz/5USuvX\nm7t5xZ6odlFcdeZVjEgeEdB6oqOjGTp0qK0yubm5tmfME2NM/zGNUs+A1n/g96fb63v7ZsSbpCaV\nkxQTuLgA07o0YgT89rcBrkg6doQ//Snw9UREwL33Br4ehwPuust+uaeeMi3VYlN5OVx4IQwcaL3M\n/Pn1/Imo+VPbuIiIiIi0OEqCRURERKTFUXcIaRbWrYN9++yVOeMM6NEjMPFIw7jdppuq3Rt1iotN\nX0iH9fvICAuDSy8FO/cULloEBw/aiy0vz15czdb69fYu3oEDkJwcuHhEqli2zP4wdg6HGSu5Xbt6\nVGZngo3du6Gb9XsEvPQhWS0lwXLCGzLE/hinGzeaIRqb+d/3Cau8HH78Ea66yl650lJ794sAzJsH\n559v7071VavMWKpt21ovc8opcNJJ9mJrdgYOtH8RkpPt3xUm0gDr1kGvXtChg/UyS5ZAdrbNJDgl\nxX5impxs/0vh4MHQpYu9Mi3kQ1JJsJzwYmPtT7iTm6ubMpo6pxNOPz3w9Xz1Vf3K9ekD7dv7N5Zm\nLybGLCJNXHIyJCRYP37t2npU0qmTWQJNH5I1Up9gEREREWlxWlxLsNvtprA+A03b5HQ6ibA5Dllh\nYQgHDpg+jYFUZn1OgGar3FXG4dJcDth4K+SX5AcuoBOE222mqa6osFeudWuwOYN2o8rOtjdsoN3n\n3xC5RblEhtnoq1FUhCPf3uwKrQ7vp6IolvLCA7bKdYjoQIhTE7RI/WRn2+++ZFdFhflVyU5//Pp+\nRublmS7sdnTsqDksKC62f+Giovwy1muLSoJDQkKIjIxkw4YNAa/r6NGjDBs2zNYMXvPmdeLnn50B\nT4KdTnt9GZujDUd+4OfDS9m3wd6FODP+zABFdGLIy4PXXrPXV66iwnyo3H9/4OJqiIQE+Pxze2Xa\ntGmcsba7tu3K0t1LbZXp9c2PtMk+TEUre/+97+x4iIINGy0fn1+Sz/k9z2dQ3CBb9YiA6eY9d27g\n6ykqMom2nRtf6/MZ2bUrLF9uFqsOHzb3PfzmN/bqalZiY03LyscfWy6Sl7ePfbFh9LnrsQZXb+V/\nyRHAi0AI8L/A1GqOeRm4BDgKjAGsT3XViJxOJwPtDDDdAIsXL7ZdxuVycOONLkv3jaSnp5Oammo/\nMAHAhYveUYO5e/DvdC1tqKgwXTrvvrvmY6pez4ICeP31wMdWX6NHBzuCml2cfLH99+fWOXDNGabT\ncgB9seWLek8fHSz6W/evhlzPG2/0byzBdv75ZrHjo48qd7ttke/PpKTaP1CqcWjJPHKWfuOX6utq\nhA8BXsEkwn2B0UDV7yyXAsnAycBYYJpfIpNapaenBzuEZkPX0r90Pf1L19N/dC39S9fTv3Q9G19d\nSfAQYCuQAZQB7wO/r3LMlcBbnvVlQAxQj9nHRUREREQaR13dIeKBXT7bu4GqnSKrO6YbsN9qEE6n\nk1Uffs7P0960WqTJy83LY/XfZ9oq0/kwrH01ktat6+68lPn9cpY8k13f8Fq82F0FuPISWPKfvWQu\nX8eS7PeCHVKDlJQUk5OTw85WCwNaT0WFg06lTjILa75J8PD335P5zDPe7dJSBx2/j2buSjt3mzjo\nlhNC5jNHGhBt81D1etYl7MABjrRpQ2mA74DN2JfBksIlfLvm24DW40/fb/meZ76wfi2ldrqeDbNu\nWyTv/ewkvJXpE/HzomVsevLlIEdl9NmxlaR9e8if9oblMm2KitjfviNrftoSwMggJmc3l8e19su5\n6rpf8g+YrhB3eLZvxCTBE3yO+Qx4Blji2f4P8CDwU5VzbQV6NSRYERERERELtmG669aorpbgPYDv\ncNEJmJbe2o7p5nmsKs17KSIiIiInhFBMJp0EtAJWU/2NcV941s8Cfmis4EREREREAuUS4BdMd4aJ\nnsfGeZZjXvHsXwMMaNToRERERERERERERCT4RgCbgC3AQ0GO5UQ3GzPqxrpgB9JMJADfAeuBn4F7\ngxvOCa81ZpjE1cAG4OnghtMshGAmH/os2IE0AxnAWsz1tDGvl9QgBvgY2Ij5ez8ruOGc0Hpj3pfH\nlsPo86ghJmI+19cB7wEBnoe3ZiGYbhJJQBjV9ykW64YDZ6Ak2F+6AP09620x3X70/myYSM+/oZj7\nA4YFMZbm4H7gXaARJpht9nYA7YMdRDPyFnCbZz0UaBfEWJoTJ7CXygMOiHVJwHaOJ74fALfUdHBd\nk2U0lJXJNsS6xUBusINoRvZhvpgBFGBaNOKCF06zcNTzbyvMl+CcIMZyouuGufH4f6l7OEuxRtfR\nP9phGmVme7bLMa2X0nAXYAYk2FXXgVKtI5h8MxLz5SyS6kcsAwKfBFc3kUZ8gOsUqY8kTCv7siDH\ncaJzYr5Y7Md0NdkQ3HBOaP8D/AVwBTuQZsKNGcd+JcfHvpf66QFkA29g5gSYyfFfgaRhRmF+wpf6\nyQGeBzKBLCAP83dfrUAnwe4An1/EH9pi+rbdh2kRlvpzYbqYdAPOBVKDGs2J63LgAKZ/oFov/eMc\nzBfdS4B7MC2ZUj+hmJGgXvP8Wwg8HNSImodWwBXAR8EO5ATWC/gTpmErDvP5fkNNBwc6CbYy2YZI\nMIUB/wTeAf4d5Fiak8PA58CgYAdygjobuBLTj3UOcB7wj6BGdOLb6/k3G/gE011P6me3Z1nh2f4Y\nDY/qD5cAP2Leo1I/g4D/Aw5huun8C/P/aVBYmWxD7ElCN8b5iwOTWPxPsANpJjpi7hgHiAAWAecH\nL5xmIwWNDtFQkUCUZ70NsAS4KHjhNAuLgFM862nA1OCF0my8Ty03cYklv8WM9hSB+Yx/C/PLT9BU\nN9mG1M8cTB+XEkxf61uDG84Jbxjm5/vVHB+aZkRQIzqxnYbpH7gaMxTVX4IbTrORgkaHaKgemPfl\naswHpD6LGu63mJbgNZjWNo0O0TBtgIMc/7Im9fcgx4dIewvzi6+IiIiIiIiIiIiIiIiIiIiIiIiI\niIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiINDH/Hwqa4jQICG+eAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c8186d0>"
       ]
      }
     ],
     "prompt_number": 77
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "** 4.Create a boxplot over the four dimensions**"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "figure(figsize=(12,3))#create a new figure    \n",
      "boxplot(data)\n",
      "title(\"Boxplot of Iris Data Set\")\n",
      "legend()\n",
      "show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAArUAAADSCAYAAABUzjiKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nUP8PgRJBj8TJCVIAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x111353710>"
       ]
      }
     ],
     "prompt_number": 83
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "** 5. Implement a function that creates a scatter plot matrix, where the main diagonal are either boxplots or histograms and classes.**\n",
      "* For all of the above plots encode the class information with different colors"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def sm(data,labels, classes=None, colors=None,num_bins=50):\n",
      "    row,column = data.shape\n",
      "    fig,axes = plt.subplots(column,column,figsize=(10,4))\n",
      "    fig.tight_layout()\n",
      "    for i in range(column):\n",
      "       for j in range(column):\n",
      "            if i==j:            \n",
      "                n, bins, patches = axes[i,j].hist(data[:,i], num_bins, normed=1, alpha=0.5)\n",
      "            else:\n",
      "                if classes is not None and colors is not None:\n",
      "                    for idx,c in enumerate(colors):\n",
      "                        mask = classes== (idx+1)\n",
      "                        axes[i,j].plot(data[mask,i],data[mask,j],\".\",color=colors[idx],label = \"%s vs. %s\"%(labels[i],labels[j]))\n",
      "                else:\n",
      "                    axes[i,j].plot(data[:,i],data[:,j],\".\",label = \"%s vs. %s\"%(labels[i],labels[j]))\n",
      "            "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 120
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sm(data,names)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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pnCQuFklyIYP4vt2E+BeFlpLMKm9htVKi5Z08pakrh4xzyPYHfgXcQfik+761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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x111a491d0>"
       ]
      }
     ],
     "prompt_number": 121
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sm(data,names,classes,[\"red\",\"green\",\"blue\"])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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PNg8/fHeH+9P9eb3qz4uxyP7f6dcvNtfx+jN0cy3A9noxDA7JR1IW1mPkQbZ2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57wTIazgVpkpS4T+Csil23AfxnGGW2JucCzwCSitCfmzpXwCoDdu6tZtKi6559EkdDE0Jaw5WlE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\n3Ei4pvul6RMnOntuy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       "text": [
        "<matplotlib.figure.Figure at 0x1111055d0>"
       ]
      }
     ],
     "prompt_number": 122
    }
   ],
   "metadata": {}
  }
 ]
}